Identification of N-Nitrosamines in Treated Drinking Water Using Nanoelectrospray Ionization High-Field Asymmetric Waveform Ion Mobility Spectrometry with Quadrupole Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
We report a nanoelectrospray ionization (nESI) with high-field asymmetric waveform ion mobility spectrometry (FAIMS) and tandem mass spectrometry (MS-MS) method for determination of small molecules of m/z 50 to 200 and its potential application in environmental analysis. Integration of nESI with FAIMS and MS-MS combines the advantages of these three techniques into one method. The nESI provides efficient sample introduction and ionization and allows for collection of multiple data from only microliters of samples. The FAIMS provides rapid separation, reduces or eliminates background interference, and improves the signal-to-noise ratio as much as 10-fold over nESI-MS-MS. The tandem quadrupole time-of-flight MS detection provides accurate mass and mass spectral measurements for structural identification. Characteristics of FAIMS compensation voltage (CV) spectra of seven nitrosamines, N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosodi-n-propylamine (NDPA), N-nitrosodi-n-butylamine (NDBA), N-nitrosopiperidine (NPip), and N-nitrosopyrrolidine (NPyr), were analyzed. The optimal CV of the nitrosamines (at DV -4000 V) were: -1.6 V, NDBA; 2.6 V, NDPA; 6.6 V, NPip; 8.8 V, NDEA; 13.2 V, NPyr; 14.4 V, NMEA; and 19.4 V, NDMA. Fragmentation patterns of the seven nitrosamines in the nESI-FAIMS-MS-MS were also obtained. The specific CV and MS-MS spectra resulted in positive identification of NPyr and NPip in a treated water sample, demonstrating the potential application of this technique in environmental analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".